Clinical laboratory analysis of biological samples is a large and important commercial activity world wide, and is fundamental for medical-services provision, public health, environmental monitoring, occupational-health-and-safety monitoring programs, provision of veterinary services, and a wide variety of important services and programs provided both by private and commercial institutions, including medical clinics and hospitals, as well as public and governmental institutions. Often, results of clinical laboratory analysis provide the data upon which important medical, public health, and governmental-policy decisions are made. Errors in clinical-laboratory results may lead to incorrect diagnosis of health, environmental, occupational-safety, and other problems, leading at least to a need for repeating expensive clinical-laboratory tests and other such inefficiencies, and potentially leading to incorrect treatments, incorrect remedial measures, and injury or harm to people, domesticated animals, and the environment. For example, of the estimated seven billion medical laboratory tests conducted in the United States each year, approximately 70 million laboratory tests are estimated to produce erroneous results. These erroneous results are thought to result in tens of billions of dollars of unnecessary economic costs each year.
Clinical laboratories well understand the problem of erroneous clinical laboratory results, and currently apply a number of different methods to control and standardize laboratory procedures to prevent errors and to analyze clinical-laboratory results for any inadvertent errors that arise despite control and standardization. One method for analyzing clinical-laboratory results is to manually analyze results and data by laboratory and medical experts. Manual analysis of laboratory results relies on analysis of trends, over time, in the results generated for particular patients and on detecting various internal inconsistencies within laboratory-results data sets. Various automated clinical-laboratory-result analysis systems and methods have been developed, most employing rule-based expert systems and pattern-detection systems.
Both manual analysis and current automated analysis systems have significant drawbacks and deficiencies. For example, manual analysis that depends on observing trends in the results generated for particular patients is highly dependent on the frequency at which results are generated for the particular patients, the inherent variability of the different types of test results, and the patients' overall conditions. As a patient's condition departs further and further from a normal, healthy state, the variability of various clinical-laboratory results generated from samples taken from the patient may often markedly increase, leading to increased unpredictability of errors in the clinical-laboratory results. Laboratory experts are generally efficient error detectors, but, as with any human activity, the accuracy of manual clinical-laboratory-result analysis may suffer from fatigued or distracted analysts and from clerical errors. As another example, when a particular type of clinical-laboratory result has relatively large, intrinsic variability, it may be difficult to spot small, systematic variations indicative of erroneous results.
While internal consistency of clinical-laboratory results is an important target for manual and automated analysis, the many and often dynamical functional dependencies between different types of clinical tests and different types of clinical-test results may be difficult to discover, difficult to apply to large data sets, and extremely difficult to capture in simple logical rules on which expert systems are based. Rule-based expert systems are often proprietary and therefore opaque to users and regulators. Rule-based expert systems are notoriously brittle with respect to addition of new rules and modification of existing rules. Small changes to the rule base may often lead to unpredictable and unintended perturbations, similar to observed instabilities in chaotic systems with respect to initial conditions. Moreover, a rule-based expert system designed to detect clinical-laboratory errors cannot generally infer likely causes for the errors.
For all of these reasons, manual analysis and currently available automated analysis systems are generally incapable of ferreting out all of the potential errors that arise in reported clinical-laboratory-analysis results. Clinical-laboratory personnel, users of clinical-laboratory results, including medical professionals, public health professionals, veterinarians, and other users, and ultimately all who undergo medical treatment, pay for medical treatments, and live and work in environments monitored for health and safety, have therefore recognized the need for continued development of more effective and efficient clinical-laboratory-result error-detection methods and systems and the need to remain ever vigilant in evaluating and using clinical-laboratory results.